Chapter 8 HMSC analysis
8.2 Variance partitioning
# Compute variance partitioning
varpart=computeVariancePartitioning(m)
varpart$vals %>%
as.data.frame() %>%
rownames_to_column(var="variable") %>%
pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
mutate(variable=factor(variable, levels=rev(c("origin","sex","logseqdepth","Random: location")))) %>%
group_by(variable) %>%
summarise(mean=mean(value)*100,sd=sd(value)*100) %>%
tt()| variable | mean | sd |
|---|---|---|
| Random: location | 37.900015 | 25.317903 |
| logseqdepth | 56.110626 | 25.796874 |
| sex | 4.937460 | 5.612719 |
| origin | 1.051899 | 1.282563 |
# Basal tree
varpart_tree <- genome_tree
#Varpart table
varpart_table <- varpart$vals %>%
as.data.frame() %>%
rownames_to_column(var="variable") %>%
pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
mutate(genome=factor(genome, levels=rev(varpart_tree$tip.label))) %>%
mutate(variable=factor(variable, levels=rev(c("origin","sex","logseqdepth","Random: location"))))
#Phylums
phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
mutate(phylum=str_remove_all(phylum, "p__"))%>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
filter(genome %in% varpart_tree$tip.label) %>%
arrange(match(genome, varpart_tree$tip.label)) %>%
mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
column_to_rownames(var = "genome") %>%
dplyr::select(phylum)
colors_alphabetic <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
mutate(phylum=str_remove_all(phylum, "p__"))%>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
filter(genome %in% varpart_tree$tip.label) %>%
arrange(match(genome, varpart_tree$tip.label)) %>%
dplyr::select(phylum, colors) %>%
unique() %>%
arrange(phylum) %>%
dplyr::select(colors) %>%
pull()
# Basal ggtree
varpart_tree <- varpart_tree %>%
force.ultrametric(.,method="extend") %>%
ggtree(., size = 0.3)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************
# Add phylum colors next to the tree tips
varpart_tree <- gheatmap(varpart_tree, phylum_colors, offset=-0.2, width=0.1, colnames=FALSE) +
scale_fill_manual(values=colors_alphabetic)+
labs(fill="Phylum")
#Reset fill scale to use a different colour profile in the heatmap
varpart_tree <- varpart_tree + new_scale_fill()
# Add variance stacked barplot
vertical_tree <- varpart_tree +
scale_fill_manual(values=c("#506a96","#cccccc","#be3e2b","#f6de6c"))+
geom_fruit(
data=varpart_table,
geom=geom_bar,
mapping = aes(x=value, y=genome, fill=variable, group=variable),
pwidth = 2,
offset = 0.05,
width= 1,
orientation="y",
stat="identity")+
labs(fill="Variable")
vertical_tree8.3 Posterior estimates
# Select desired support threshold
support=0.9
negsupport=1-support
# Basal tree
postestimates_tree <- genome_tree
# Posterior estimate table
post_beta <- getPostEstimate(hM=m, parName="Beta")$support %>%
as.data.frame() %>%
mutate(variable=m$covNames) %>%
pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
mutate(genome=factor(genome, levels=rev(postestimates_tree$tip.label))) %>%
mutate(value = case_when(
value >= support ~ "Positive",
value <= negsupport ~ "Negative",
TRUE ~ "Neutral")) %>%
mutate(value=factor(value, levels=c("Positive","Neutral","Negative"))) %>%
pivot_wider(names_from = variable, values_from = value) %>%
#select(genome,sp_vulgaris,area_semi,area_urban,sp_vulgarisxarea_semi,sp_vulgarisxarea_urban,season_spring,season_winter,sp_vulgarisxseason_spring,sp_vulgarisxseason_winter) %>%
column_to_rownames(var="genome")
#Phylums
phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
mutate(phylum=str_remove_all(phylum, "p__")) %>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
filter(genome %in% postestimates_tree$tip.label) %>%
arrange(match(genome, postestimates_tree$tip.label)) %>%
mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
column_to_rownames(var = "genome") %>%
dplyr::select(phylum)
colors_alphabetic <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
mutate(phylum=str_remove_all(phylum, "p__")) %>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
filter(genome %in% postestimates_tree$tip.label) %>%
arrange(match(genome, postestimates_tree$tip.label)) %>%
dplyr::select(phylum, colors) %>%
unique() %>%
arrange(phylum) %>%
dplyr::select(colors) %>%
pull()
# Basal ggtree
postestimates_tree <- postestimates_tree %>%
force.ultrametric(.,method="extend") %>%
ggtree(., size = 0.3)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************
#Add phylum colors next to the tree tips
postestimates_tree <- gheatmap(postestimates_tree, phylum_colors, offset=-0.2, width=0.1, colnames=FALSE) +
scale_fill_manual(values=colors_alphabetic)+
labs(fill="Phylum")
#Reset fill scale to use a different colour profile in the heatmap
postestimates_tree <- postestimates_tree + new_scale_fill()
# Add posterior significant heatmap
postestimates_tree <- gheatmap(postestimates_tree, post_beta, offset=0, width=0.5, colnames=TRUE, colnames_position="top",colnames_angle=90, colnames_offset_y=1, hjust=0) +
scale_fill_manual(values=c("#be3e2b","#f4f4f4","#b2b530"))+
labs(fill="Trend")
postestimates_tree +
vexpand(.25, 1) # expand top 8.3.1 Origin
8.3.1.1 Positively associated genomes with domestic cats
getPostEstimate(hM=m, parName="Beta")$support %>%
as.data.frame() %>%
mutate(variable=m$covNames) %>%
pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
mutate(trend = case_when(
value >= support ~ "Positive",
value <= negsupport ~ "Negative",
TRUE ~ "Neutral")) %>%
filter(variable=="originTame", trend=="Positive") %>%
arrange(-value) %>%
left_join(genome_metadata,by=join_by(genome==genome)) %>%
dplyr::select(genome,phylum,class,order,family,species,value) %>%
arrange(phylum, class, family)%>%
tt()| genome | phylum | class | order | family | species | value |
|---|---|---|---|---|---|---|
| bin_CaboVerde.50 | Actinomycetota | Actinomycetia | Actinomycetales | Actinomycetaceae | NA | 0.953 |
| bin_Aruba.25 | Actinomycetota | Actinomycetia | Actinomycetales | Bifidobacteriaceae | Bifidobacterium pseudocatenulatum | 0.995 |
| bin_Aruba.2 | Actinomycetota | Actinomycetia | Actinomycetales | Bifidobacteriaceae | Bifidobacterium adolescentis | 0.994 |
| bin_Aruba.6 | Actinomycetota | Actinomycetia | Actinomycetales | Bifidobacteriaceae | Bifidobacterium longum | 0.985 |
| bin_Denmark.14 | Actinomycetota | Actinomycetia | Actinomycetales | Bifidobacteriaceae | Bifidobacterium gallinarum | 0.950 |
| bin_Aruba.47 | Actinomycetota | Actinomycetia | Mycobacteriales | Mycobacteriaceae | NA | 0.964 |
| bin_Aruba.57 | Actinomycetota | Actinomycetia | Mycobacteriales | Mycobacteriaceae | Corynebacterium pyruviciproducens | 0.931 |
| bin_Aruba.51 | Actinomycetota | Coriobacteriia | Coriobacteriales | Atopobiaceae | Parolsenella sp900544655 | 0.969 |
| bin_Denmark.44 | Actinomycetota | Coriobacteriia | Coriobacteriales | Atopobiaceae | UBA7748 sp900314535 | 0.951 |
| bin_Aruba.14 | Actinomycetota | Coriobacteriia | Coriobacteriales | Coriobacteriaceae | Collinsella stercoris | 1.000 |
| bin_Denmark.4 | Actinomycetota | Coriobacteriia | Coriobacteriales | Coriobacteriaceae | Collinsella stercoris | 1.000 |
| bin_Aruba.21 | Actinomycetota | Coriobacteriia | Coriobacteriales | Coriobacteriaceae | NA | 0.999 |
| bin_Brazil.61 | Actinomycetota | Coriobacteriia | Coriobacteriales | Coriobacteriaceae | Collinsella tanakaei | 0.998 |
| bin_Aruba.39 | Actinomycetota | Coriobacteriia | Coriobacteriales | Coriobacteriaceae | Collinsella sp902362275 | 0.992 |
| bin_Spain.84 | Actinomycetota | Coriobacteriia | Coriobacteriales | Coriobacteriaceae | Collinsella sp900555555 | 0.992 |
| bin_Brazil.151 | Actinomycetota | Coriobacteriia | Coriobacteriales | Coriobacteriaceae | Collinsella intestinalis | 0.991 |
| bin_Denmark.127 | Actinomycetota | Coriobacteriia | Coriobacteriales | Coriobacteriaceae | Collinsella sp900548365 | 0.991 |
| bin_Denmark.17 | Actinomycetota | Coriobacteriia | Coriobacteriales | Coriobacteriaceae | Collinsella bouchesdurhonensis | 0.983 |
| bin_Brazil.81 | Bacillota | Bacilli | RFN20 | CAG-826 | NA | 0.907 |
| bin_Brazil.109 | Bacillota_A | Clostridia | Oscillospirales | Butyricicoccaceae | AM07-15 sp003477405 | 0.936 |
| bin_Aruba.28 | Bacillota_A | Clostridia | Oscillospirales | Oscillospiraceae | NA | 0.995 |
| bin_Malaysia.9 | Bacillota_A | Clostridia | Oscillospirales | Oscillospiraceae | Dysosmobacter welbionis | 0.986 |
| bin_Malaysia.116 | Bacillota_A | Clostridia | Oscillospirales | Oscillospiraceae | Flavonifractor plautii | 0.981 |
| bin_Malaysia.34 | Bacillota_A | Clostridia | Oscillospirales | Oscillospiraceae | Lawsonibacter sp000177015 | 0.954 |
| bin_Aruba.54 | Bacillota_A | Clostridia | Tissierellales | Peptoniphilaceae | NA | 0.959 |
| bin_CaboVerde.35 | Bacillota_A | Clostridia | Tissierellales | Peptoniphilaceae | NA | 0.930 |
| bin_Aruba.36 | Bacillota_A | Clostridia | Tissierellales | Peptoniphilaceae | Peptoniphilus_C sp902363535 | 0.913 |
| bin_Aruba.52 | Bacillota_A | Clostridia | Tissierellales | Peptoniphilaceae | NA | 0.906 |
| bin_Malaysia.26 | Bacillota_A | Clostridia | Oscillospirales | Ruminococcaceae | NA | 0.976 |
| bin_Aruba.29 | Bacillota_A | Clostridia | Oscillospirales | Ruminococcaceae | Gemmiger variabilis_C | 0.941 |
| bin_Malaysia.103 | Bacillota_C | Negativicutes | Acidaminococcales | Acidaminococcaceae | Acidaminococcus intestini | 0.942 |
| bin_Malaysia.8 | Bacillota_C | Negativicutes | Veillonellales | Dialisteraceae | Dialister sp900543165 | 0.981 |
| bin_Denmark.60 | Bacillota_C | Negativicutes | Veillonellales | Dialisteraceae | Allisonella histaminiformans | 0.938 |
| bin_Aruba.64 | Bacillota_C | Negativicutes | Veillonellales | Megasphaeraceae | Megasphaera sp000417505 | 0.970 |
| bin_Brazil.58 | Bacillota_C | Negativicutes | Veillonellales | Megasphaeraceae | Megasphaera elsdenii | 0.969 |
| bin_CaboVerde.38 | Bacillota_C | Negativicutes | Veillonellales | Megasphaeraceae | Megasphaera sp000417505 | 0.961 |
| bin_Malaysia.81 | Bacillota_C | Negativicutes | Veillonellales | Megasphaeraceae | Megasphaera stantonii | 0.943 |
| bin_Malaysia.96 | Bacillota_C | Negativicutes | Veillonellales | Megasphaeraceae | Caecibacter sp003467125 | 0.919 |
| bin_Brazil.163 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotella lascolaii | 0.992 |
| bin_CaboVerde.18 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotella copri | 0.981 |
| bin_Aruba.10 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotella sp900544825 | 0.978 |
| bin_Brazil.5 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotella sp900540415 | 0.929 |
| bin_Spain.21 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Phocaeicola coprophilus | 0.929 |
| bin_Malaysia.18 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotellamassilia sp000437675 | 0.924 |
| bin_Malaysia.117 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotellamassilia sp900541335 | 0.919 |
| bin_Malaysia.77 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Phocaeicola sp900542985 | 0.913 |
| bin_Brazil.75 | Campylobacterota | Campylobacteria | Campylobacterales | Helicobacteraceae | NA | 0.964 |
| bin_Malaysia.61 | Campylobacterota | Campylobacteria | Campylobacterales | Helicobacteraceae | Helicobacter_C labetoulli | 0.964 |
| bin_Brazil.128 | Campylobacterota | Campylobacteria | Campylobacterales | Helicobacteraceae | NA | 0.963 |
| bin_Brazil.70 | Desulfobacterota | Desulfovibrionia | Desulfovibrionales | Desulfovibrionaceae | Mailhella sp003150275 | 0.944 |
| bin_Brazil.146 | Pseudomonadota | Alphaproteobacteria | RF32 | CAG-239 | CAG-495 sp001917125 | 0.969 |
| bin_Brazil.9 | Pseudomonadota | Alphaproteobacteria | RF32 | CAG-239 | CAG-495 sp000436375 | 0.919 |
| bin_Aruba.15 | Pseudomonadota | Gammaproteobacteria | Burkholderiales | Burkholderiaceae | NA | 0.963 |
| bin_Brazil.51 | Pseudomonadota | Gammaproteobacteria | Burkholderiales | Burkholderiaceae | Sutterella wadsworthensis_A | 0.963 |
| bin_Spain.43 | Pseudomonadota | Gammaproteobacteria | Burkholderiales | Burkholderiaceae | CAG-521 sp000437635 | 0.959 |
| bin_Brazil.64 | Pseudomonadota | Gammaproteobacteria | Burkholderiales | Burkholderiaceae | NA | 0.948 |
| bin_Brazil.92 | Pseudomonadota | Gammaproteobacteria | Burkholderiales | Burkholderiaceae | CAG-521 sp000437635 | 0.948 |
| bin_Brazil.15 | Pseudomonadota | Gammaproteobacteria | Burkholderiales | Burkholderiaceae | NA | 0.930 |
| bin_CaboVerde.33 | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Succinivibrionaceae | Anaerobiospirillum sp900543125 | 0.995 |
| bin_Brazil.82 | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Succinivibrionaceae | Anaerobiospirillum succiniciproducens | 0.990 |
| bin_CaboVerde.55 | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Succinivibrionaceae | Anaerobiospirillum_A thomasii | 0.939 |
8.3.1.2 Positively associated genomes feral cats
getPostEstimate(hM=m, parName="Beta")$support %>%
as.data.frame() %>%
mutate(variable=m$covNames) %>%
pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
mutate(trend = case_when(
value >= support ~ "Positive",
value <= negsupport ~ "Negative",
TRUE ~ "Neutral")) %>%
filter(variable=="originTame", trend=="Negative") %>%
arrange(value) %>%
left_join(genome_metadata,by=join_by(genome==genome)) %>%
dplyr::select(genome,phylum,class,order,family,species,value) %>%
arrange(phylum,class,family)%>%
tt()| genome | phylum | class | order | family | species | value |
|---|---|---|---|---|---|---|
| bin_Denmark.27 | Bacillota | Bacilli | Lactobacillales | Enterococcaceae | Enterococcus_B hirae | 0.000 |
| bin_Brazil.12 | Bacillota | Bacilli | Lactobacillales | Enterococcaceae | Enterococcus faecalis | 0.001 |
| bin_Brazil.170 | Bacillota | Bacilli | Lactobacillales | Enterococcaceae | NA | 0.014 |
| bin_CaboVerde.24 | Bacillota | Bacilli | Lactobacillales | Enterococcaceae | Enterococcus_E cecorum | 0.020 |
| bin_CaboVerde.16 | Bacillota | Bacilli | Lactobacillales | Lactobacillaceae | Limosilactobacillus reuteri | 0.000 |
| bin_Denmark.56 | Bacillota | Bacilli | Lactobacillales | Lactobacillaceae | Levilactobacillus brevis | 0.000 |
| bin_Malaysia.72 | Bacillota | Bacilli | Lactobacillales | Lactobacillaceae | Limosilactobacillus reuteri | 0.000 |
| bin_CaboVerde.60 | Bacillota | Bacilli | Lactobacillales | Lactobacillaceae | Ligilactobacillus agilis | 0.001 |
| bin_Malaysia.127 | Bacillota | Bacilli | Lactobacillales | Lactobacillaceae | Ligilactobacillus agilis | 0.001 |
| bin_Denmark.61 | Bacillota | Bacilli | Lactobacillales | Lactobacillaceae | Latilactobacillus sakei | 0.003 |
| bin_Malaysia.35 | Bacillota | Bacilli | Lactobacillales | Lactobacillaceae | Ligilactobacillus animalis | 0.003 |
| bin_Malaysia.4 | Bacillota | Bacilli | Lactobacillales | Lactobacillaceae | Ligilactobacillus ruminis | 0.035 |
| bin_CaboVerde.14 | Bacillota | Bacilli | Lactobacillales | Lactobacillaceae | Lactobacillus acidophilus | 0.067 |
| bin_Aruba.18 | Bacillota | Bacilli | Lactobacillales | Streptococcaceae | Streptococcus lutetiensis | 0.001 |
| bin_Brazil.22 | Bacillota | Bacilli | Lactobacillales | Streptococcaceae | Streptococcus pasteurianus | 0.001 |
| bin_Denmark.117 | Bacillota | Bacilli | Lactobacillales | Streptococcaceae | Lactococcus garvieae | 0.001 |
| bin_Denmark.113 | Bacillota | Bacilli | Lactobacillales | Streptococcaceae | Streptococcus alactolyticus | 0.002 |
| bin_Brazil.69 | Bacillota_A | Clostridia | Oscillospirales | Acutalibacteraceae | Clostridium_A leptum | 0.097 |
| bin_Denmark.93 | Bacillota_A | Clostridia | Lachnospirales | Anaerotignaceae | Anaerotignum sp001304995 | 0.074 |
| bin_CaboVerde.3 | Bacillota_A | Clostridia | Clostridiales | Clostridiaceae | Clostridium_P perfringens | 0.000 |
| bin_Denmark.42 | Bacillota_A | Clostridia | Clostridiales | Clostridiaceae | Clostridium_P perfringens | 0.000 |
| bin_Brazil.136 | Bacillota_A | Clostridia | Clostridiales | Clostridiaceae | Clostridium thermobutyricum | 0.041 |
| bin_Brazil.3 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Faecalimonas sp900555395 | 0.026 |
| bin_Brazil.89 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Faecalimonas sp900550235 | 0.034 |
| bin_Denmark.63 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Anaerostipes hadrus | 0.040 |
| bin_Denmark.19 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Faecalimonas sp900556835 | 0.052 |
| bin_Denmark.118 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Dorea_A longicatena | 0.067 |
| bin_Malaysia.108 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.081 |
| bin_Brazil.113 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Anaerobutyricum sp900754855 | 0.095 |
| bin_Denmark.50 | Bacillota_A | Clostridia | Peptostreptococcales | Peptostreptococcaceae | Peptacetobacter sp900539645 | 0.053 |
| bin_Brazil.107 | Bacillota_A | Clostridia | Monoglobales_A | UBA1381 | CAG-41 sp900066215 | 0.016 |
| bin_Brazil.159 | Fusobacteriota | Fusobacteriia | Fusobacteriales | Fusobacteriaceae | Fusobacterium_B sp900541465 | 0.040 |
| bin_Brazil.140 | Fusobacteriota | Fusobacteriia | Fusobacteriales | Fusobacteriaceae | Fusobacterium_A sp900543175 | 0.046 |
| bin_Malaysia.6 | Fusobacteriota | Fusobacteriia | Fusobacteriales | Fusobacteriaceae | Fusobacterium_B sp900545035 | 0.088 |
8.3.1.3 Estimate - support plot
estimate <- getPostEstimate(hM=m, parName="Beta")$mean %>%
as.data.frame() %>%
mutate(variable=m$covNames) %>%
filter(variable=="originTame") %>%
pivot_longer(!variable, names_to = "genome", values_to = "mean") %>%
dplyr::select(genome,mean)
support <- getPostEstimate(hM=m, parName="Beta")$support %>%
as.data.frame() %>%
mutate(variable=m$covNames) %>%
filter(variable=="originTame") %>%
pivot_longer(!variable, names_to = "genome", values_to = "support") %>%
dplyr::select(genome,support)
phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
mutate(phylum=str_remove_all(phylum, "p__")) %>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
filter(genome %in% estimate$genome) %>%
arrange(match(genome, estimate$genome)) %>%
dplyr::select(phylum, colors) %>%
unique() %>%
arrange(phylum) %>%
dplyr::select(colors) %>%
pull()
inner_join(estimate,support,by=join_by(genome==genome)) %>%
mutate(significance=ifelse(support >= 0.9 | support <= 0.1,1,0)) %>%
mutate(support=ifelse(mean<0,1-support,support)) %>%
left_join(genome_metadata, by = join_by(genome == genome)) %>%
mutate(phylum = ifelse(support > 0.9, phylum, NA)) %>%
ggplot(aes(x=mean,y=support,color=phylum))+
geom_point(alpha=0.7, shape=16, size=3)+
scale_color_manual(values = phylum_colors) +
geom_vline(xintercept = 0) +
xlim(c(-0.4,0.4)) +
labs(x = "Beta regression coefficient", y = "Posterior probability") +
theme_minimal()+
theme(legend.position = "none")8.3.1.4 Correlations
#Compute the residual correlation matrix
OmegaCor = computeAssociations(m)
# Refernece tree (for sorting genomes)
genome_tree_subset <- genome_tree %>%
keep.tip(., tip=m$spNames)
#Co-occurrence matrix at the animal level
supportLevel = 0.95
toPlot = ((OmegaCor[[1]]$support>supportLevel)
+ (OmegaCor[[1]]$support<(1-supportLevel))>0)*OmegaCor[[1]]$mean
matrix <- toPlot %>%
as.data.frame() %>%
rownames_to_column(var="genome1") %>%
pivot_longer(!genome1, names_to = "genome2", values_to = "cor") %>%
mutate(genome1= factor(genome1, levels=genome_tree_subset$tip.label)) %>%
mutate(genome2= factor(genome2, levels=genome_tree_subset$tip.label)) %>%
ggplot(aes(x = genome1, y = genome2, fill = cor)) +
geom_tile() +
scale_fill_gradient2(low = "#be3e2b",
mid = "#f4f4f4",
high = "#b2b530")+
theme_void()
htree <- genome_tree_subset %>%
force.ultrametric(.,method="extend") %>%
ggtree(.)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************
***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************
#create composite figure
grid.arrange(grobs = list(matrix,vtree),
layout_matrix = rbind(c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1)))8.3.1.5 Predict responses (origin)
# Select modelchain of interest
load("hmsc/fit_model1_250_10.Rdata")
gradient = c("domestic","feral")
gradientlength = length(gradient)
#Treatment-specific gradient predictions
pred <- constructGradient(m,
focalVariable = "origin",
non.focalVariables = list(logseqdepth=list(1),location=list(1))) %>%
predict(m, Gradient = ., expected = TRUE) %>%
do.call(rbind,.) %>%
as.data.frame() %>%
mutate(origin=rep(gradient,1000)) %>%
pivot_longer(!origin,names_to = "genome", values_to = "value")# weights: 9 (4 variable)
initial value 101.072331
final value 91.392443
converged
8.3.1.6 Element level
elements_table <- genome_gifts %>%
to.elements(., GIFT_db) %>%
as.data.frame()
community_elements <- pred %>%
group_by(origin, genome) %>%
mutate(row_id = row_number()) %>%
pivot_wider(names_from = genome, values_from = value) %>%
ungroup() %>%
group_split(row_id) %>%
as.list() %>%
lapply(., FUN = function(x){x %>%
dplyr::select(-row_id) %>%
column_to_rownames(var = "origin") %>%
as.data.frame() %>%
exp() %>%
t() %>%
tss() %>%
to.community(elements_table,.,GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="origin")
})
calculate_slope <- function(x) {
lm_fit <- lm(unlist(x) ~ seq_along(unlist(x)))
coef(lm_fit)[2]
}
element_predictions <- map_dfc(community_elements, function(mat) {
mat %>%
column_to_rownames(var = "origin") %>%
t() %>%
as.data.frame() %>%
rowwise() %>%
mutate(slope = calculate_slope(c_across(everything()))) %>%
dplyr::select(slope) }) %>%
t() %>%
as.data.frame() %>%
set_names(colnames(community_elements[[1]])[-1]) %>%
rownames_to_column(var="iteration") %>%
pivot_longer(!iteration, names_to="trait",values_to="value") %>%
group_by(trait) %>%
summarise(mean=mean(value),
p1 = quantile(value, probs = 0.1),
p9 = quantile(value, probs = 0.9),
positive_support = sum(value > 0)/1000,
negative_support = sum(value < 0)/1000) %>%
arrange(-positive_support)8.3.1.6.1 Positive associated functions at element level
# Positively associated
unique_funct_db<- GIFT_db[c(2,4,5,6)] %>%
distinct(Code_element, .keep_all = TRUE)
element_predictions %>%
filter(mean >0) %>%
arrange(-positive_support) %>%
filter(positive_support>=0.9) %>%
left_join(unique_funct_db, by = join_by(trait == Code_element))%>%
arrange(Domain,Function)%>%
tt()| trait | mean | p1 | p9 | positive_support | negative_support | Domain | Function | Element |
|---|---|---|---|---|---|---|---|---|
| B0103 | 0.008668973 | 0.0005965939 | 0.016001173 | 0.915 | 0.085 | Biosynthesis | Nucleic acid biosynthesis | UDP/UTP |
| D0504 | 0.004647085 | 0.0003771070 | 0.009768176 | 0.922 | 0.078 | Degradation | Amino acid degradation | Methionine |
| D0906 | 0.003822519 | 0.0002160812 | 0.008201395 | 0.932 | 0.068 | Degradation | Antibiotic degradation | Fosfomycin |
| D0205 | 0.012204272 | 0.0019631234 | 0.022408114 | 0.950 | 0.050 | Degradation | Polysaccharide degradation | Pectin |
| D0208 | 0.009799367 | 0.0014936231 | 0.017659224 | 0.939 | 0.061 | Degradation | Polysaccharide degradation | Mixed-Linkage glucans |
element_predictions %>%
filter(mean <0) %>%
arrange(-negative_support) %>%
filter(negative_support>=0.9) %>%
left_join(unique_funct_db, by = join_by(trait == Code_element))%>%
arrange(Domain,Function)%>%
tt()| trait | mean | p1 | p9 | positive_support | negative_support | Domain | Function | Element |
|---|---|---|---|---|---|---|---|---|
| B0219 | -0.004132651 | -0.009242449 | -2.704579e-04 | 0.044 | 0.956 | Biosynthesis | Amino acid biosynthesis | GABA |
| B0214 | -0.021408344 | -0.038627047 | -4.942490e-03 | 0.059 | 0.941 | Biosynthesis | Amino acid biosynthesis | Glutamate |
| B0204 | -0.016044178 | -0.032154456 | -1.338553e-03 | 0.082 | 0.918 | Biosynthesis | Amino acid biosynthesis | Serine |
| B0302 | -0.004993799 | -0.010547888 | -6.852718e-04 | 0.032 | 0.968 | Biosynthesis | Amino acid derivative biosynthesis | Betaine |
| B0310 | -0.012631605 | -0.022578453 | -3.174742e-03 | 0.035 | 0.965 | Biosynthesis | Amino acid derivative biosynthesis | Tryptamine |
| B0303 | -0.011515181 | -0.020902183 | -2.246292e-03 | 0.066 | 0.934 | Biosynthesis | Amino acid derivative biosynthesis | Ectoine |
| B0309 | -0.007441518 | -0.015073476 | -1.638992e-04 | 0.095 | 0.905 | Biosynthesis | Amino acid derivative biosynthesis | Putrescine |
| B0804 | -0.016343376 | -0.029354205 | -3.591691e-03 | 0.049 | 0.951 | Biosynthesis | Aromatic compound biosynthesis | Dipicolinate |
| B0603 | -0.016580393 | -0.031741463 | -2.636490e-03 | 0.060 | 0.940 | Biosynthesis | Organic anion biosynthesis | Citrate |
| B0601 | -0.009240453 | -0.017544737 | -1.391204e-03 | 0.069 | 0.931 | Biosynthesis | Organic anion biosynthesis | Succinate |
| B0401 | -0.011102735 | -0.022069357 | -2.102256e-04 | 0.094 | 0.906 | Biosynthesis | SCFA biosynthesis | Acetate |
| B0709 | -0.002073624 | -0.003638992 | -6.474296e-04 | 0.033 | 0.967 | Biosynthesis | Vitamin biosynthesis | Tocopherol/tocotorienol (E) |
| D0517 | -0.004495743 | -0.007874479 | -1.353705e-03 | 0.023 | 0.977 | Degradation | Amino acid degradation | Ornithine |
| D0508 | -0.003245058 | -0.007138266 | -1.816237e-04 | 0.079 | 0.921 | Degradation | Amino acid degradation | Lysine |
| D0512 | -0.018089246 | -0.035972853 | -1.186182e-03 | 0.086 | 0.914 | Degradation | Amino acid degradation | Histidine |
| D0903 | -0.004117914 | -0.009256580 | -2.622164e-04 | 0.044 | 0.956 | Degradation | Antibiotic degradation | Cephalosporin |
| D0908 | -0.015447072 | -0.027438899 | -3.764076e-03 | 0.056 | 0.944 | Degradation | Antibiotic degradation | Macrolide |
| D0601 | -0.009517923 | -0.017285585 | -2.651368e-03 | 0.024 | 0.976 | Degradation | Nitrogen compound degradation | Nitrate |
| D0603 | -0.001981904 | -0.003844551 | -3.973787e-04 | 0.036 | 0.964 | Degradation | Nitrogen compound degradation | Urate |
| D0610 | -0.002955038 | -0.004863991 | -1.021881e-03 | 0.039 | 0.961 | Degradation | Nitrogen compound degradation | Methylamine |
| D0611 | -0.004117914 | -0.009256580 | -2.622164e-04 | 0.044 | 0.956 | Degradation | Nitrogen compound degradation | Phenylethylamine |
| D0606 | -0.005881451 | -0.011632320 | -9.896364e-04 | 0.063 | 0.937 | Degradation | Nitrogen compound degradation | Allantoin |
| D0612 | -0.001574312 | -0.002955348 | -1.518534e-04 | 0.076 | 0.924 | Degradation | Nitrogen compound degradation | Hypotaurine |
| D0801 | -0.001305868 | -0.002159595 | -9.074796e-05 | 0.008 | 0.992 | Degradation | Xenobiotic degradation | Toluene |
| D0802 | -0.001305868 | -0.002159595 | -9.074796e-05 | 0.008 | 0.992 | Degradation | Xenobiotic degradation | Xylene |
| D0807 | -0.004098974 | -0.008643808 | -7.646272e-04 | 0.043 | 0.957 | Degradation | Xenobiotic degradation | Catechol |
| D0817 | -0.004953919 | -0.010591016 | -6.720724e-04 | 0.049 | 0.951 | Degradation | Xenobiotic degradation | Trans-cinnamate |
| D0816 | -0.005871819 | -0.011500014 | -4.581529e-04 | 0.083 | 0.917 | Degradation | Xenobiotic degradation | Phenylacetate |
8.3.1.6.2 Negatively associated functions at element level
element_predictions %>%
filter(mean <0) %>%
arrange(-negative_support) %>%
filter(negative_support>=0.9) %>%
left_join(unique_funct_db, by = join_by(trait == Code_element))%>%
arrange(Domain,Function)%>%
tt()| trait | mean | p1 | p9 | positive_support | negative_support | Domain | Function | Element |
|---|---|---|---|---|---|---|---|---|
| B0219 | -0.004132651 | -0.009242449 | -2.704579e-04 | 0.044 | 0.956 | Biosynthesis | Amino acid biosynthesis | GABA |
| B0214 | -0.021408344 | -0.038627047 | -4.942490e-03 | 0.059 | 0.941 | Biosynthesis | Amino acid biosynthesis | Glutamate |
| B0204 | -0.016044178 | -0.032154456 | -1.338553e-03 | 0.082 | 0.918 | Biosynthesis | Amino acid biosynthesis | Serine |
| B0302 | -0.004993799 | -0.010547888 | -6.852718e-04 | 0.032 | 0.968 | Biosynthesis | Amino acid derivative biosynthesis | Betaine |
| B0310 | -0.012631605 | -0.022578453 | -3.174742e-03 | 0.035 | 0.965 | Biosynthesis | Amino acid derivative biosynthesis | Tryptamine |
| B0303 | -0.011515181 | -0.020902183 | -2.246292e-03 | 0.066 | 0.934 | Biosynthesis | Amino acid derivative biosynthesis | Ectoine |
| B0309 | -0.007441518 | -0.015073476 | -1.638992e-04 | 0.095 | 0.905 | Biosynthesis | Amino acid derivative biosynthesis | Putrescine |
| B0804 | -0.016343376 | -0.029354205 | -3.591691e-03 | 0.049 | 0.951 | Biosynthesis | Aromatic compound biosynthesis | Dipicolinate |
| B0603 | -0.016580393 | -0.031741463 | -2.636490e-03 | 0.060 | 0.940 | Biosynthesis | Organic anion biosynthesis | Citrate |
| B0601 | -0.009240453 | -0.017544737 | -1.391204e-03 | 0.069 | 0.931 | Biosynthesis | Organic anion biosynthesis | Succinate |
| B0401 | -0.011102735 | -0.022069357 | -2.102256e-04 | 0.094 | 0.906 | Biosynthesis | SCFA biosynthesis | Acetate |
| B0709 | -0.002073624 | -0.003638992 | -6.474296e-04 | 0.033 | 0.967 | Biosynthesis | Vitamin biosynthesis | Tocopherol/tocotorienol (E) |
| D0517 | -0.004495743 | -0.007874479 | -1.353705e-03 | 0.023 | 0.977 | Degradation | Amino acid degradation | Ornithine |
| D0508 | -0.003245058 | -0.007138266 | -1.816237e-04 | 0.079 | 0.921 | Degradation | Amino acid degradation | Lysine |
| D0512 | -0.018089246 | -0.035972853 | -1.186182e-03 | 0.086 | 0.914 | Degradation | Amino acid degradation | Histidine |
| D0903 | -0.004117914 | -0.009256580 | -2.622164e-04 | 0.044 | 0.956 | Degradation | Antibiotic degradation | Cephalosporin |
| D0908 | -0.015447072 | -0.027438899 | -3.764076e-03 | 0.056 | 0.944 | Degradation | Antibiotic degradation | Macrolide |
| D0601 | -0.009517923 | -0.017285585 | -2.651368e-03 | 0.024 | 0.976 | Degradation | Nitrogen compound degradation | Nitrate |
| D0603 | -0.001981904 | -0.003844551 | -3.973787e-04 | 0.036 | 0.964 | Degradation | Nitrogen compound degradation | Urate |
| D0610 | -0.002955038 | -0.004863991 | -1.021881e-03 | 0.039 | 0.961 | Degradation | Nitrogen compound degradation | Methylamine |
| D0611 | -0.004117914 | -0.009256580 | -2.622164e-04 | 0.044 | 0.956 | Degradation | Nitrogen compound degradation | Phenylethylamine |
| D0606 | -0.005881451 | -0.011632320 | -9.896364e-04 | 0.063 | 0.937 | Degradation | Nitrogen compound degradation | Allantoin |
| D0612 | -0.001574312 | -0.002955348 | -1.518534e-04 | 0.076 | 0.924 | Degradation | Nitrogen compound degradation | Hypotaurine |
| D0801 | -0.001305868 | -0.002159595 | -9.074796e-05 | 0.008 | 0.992 | Degradation | Xenobiotic degradation | Toluene |
| D0802 | -0.001305868 | -0.002159595 | -9.074796e-05 | 0.008 | 0.992 | Degradation | Xenobiotic degradation | Xylene |
| D0807 | -0.004098974 | -0.008643808 | -7.646272e-04 | 0.043 | 0.957 | Degradation | Xenobiotic degradation | Catechol |
| D0817 | -0.004953919 | -0.010591016 | -6.720724e-04 | 0.049 | 0.951 | Degradation | Xenobiotic degradation | Trans-cinnamate |
| D0816 | -0.005871819 | -0.011500014 | -4.581529e-04 | 0.083 | 0.917 | Degradation | Xenobiotic degradation | Phenylacetate |
positive <- element_predictions %>%
filter(mean >0) %>%
arrange(mean) %>%
filter(positive_support>=0.9) %>%
dplyr::select(-negative_support) %>%
rename(support=positive_support)
negative <- element_predictions %>%
filter(mean <0) %>%
arrange(mean) %>%
filter(negative_support>=0.9) %>%
dplyr::select(-positive_support) %>%
rename(support=negative_support)
bind_rows(positive,negative) %>%
left_join(GIFT_db,by=join_by(trait==Code_element)) %>%
mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait)))) %>%
ggplot(aes(x=mean, y=fct_rev(trait), xmin=p1, xmax=p9, color=Function)) +
geom_point() +
geom_errorbar() +
xlim(c(-0.04,0.04)) +
geom_vline(xintercept=0) +
scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
theme_minimal() +
labs(x="Regression coefficient",y="Functional trait")table <- bind_rows(positive,negative) %>%
left_join(unique_funct_db,by=join_by(trait==Code_element)) %>%
mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait))))
table %>%
mutate(Element=factor(Element,levels=c(table$Element))) %>%
ggplot(aes(x=mean, y=fct_rev(Element), xmin=p1, xmax=p9, color=Function)) +
geom_point() +
geom_errorbar() +
xlim(c(-0.04,0.04)) +
geom_vline(xintercept=0) +
scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
theme_minimal() +
labs(x="Regression coefficient",y="Functional trait")8.3.1.7 Function level
functions_table <- elements_table %>%
to.functions(., GIFT_db) %>%
as.data.frame()
community_functions <- pred %>%
group_by(origin, genome) %>%
mutate(row_id = row_number()) %>%
pivot_wider(names_from = genome, values_from = value) %>%
ungroup() %>%
group_split(row_id) %>%
as.list() %>%
lapply(., FUN = function(x){x %>%
dplyr::select(-row_id) %>%
column_to_rownames(var = "origin") %>%
as.data.frame() %>%
exp() %>%
t() %>%
tss() %>%
to.community(functions_table,.,GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="origin")
})#max-min option
calculate_slope <- function(x) {
lm_fit <- lm(unlist(x) ~ seq_along(unlist(x)))
coef(lm_fit)[2]
}
function_predictions <- map_dfc(community_functions, function(mat) {
mat %>%
column_to_rownames(var = "origin") %>%
t() %>%
as.data.frame() %>%
rowwise() %>%
mutate(slope = calculate_slope(c_across(everything()))) %>%
dplyr::select(slope) }) %>%
t() %>%
as.data.frame() %>%
set_names(colnames(community_functions[[1]])[-1]) %>%
rownames_to_column(var="iteration") %>%
pivot_longer(!iteration, names_to="trait",values_to="value") %>%
group_by(trait) %>%
summarise(mean=mean(value),
p1 = quantile(value, probs = 0.1),
p9 = quantile(value, probs = 0.9),
positive_support = sum(value > 0)/1000,
negative_support = sum(value < 0)/1000) %>%
arrange(-positive_support)
# Positively associated
function_predictions %>%
filter(mean >0) %>%
arrange(-positive_support) %>%
tt()| trait | mean | p1 | p9 | positive_support | negative_support |
|---|---|---|---|---|---|
| D02 | 8.252038e-03 | -0.0029397646 | 0.0196760509 | 0.822 | 0.178 |
| B08 | 7.109220e-03 | -0.0040068882 | 0.0171378858 | 0.770 | 0.230 |
| B01 | 1.216320e-03 | -0.0059241188 | 0.0081540077 | 0.610 | 0.390 |
| S01 | 1.014944e-03 | -0.0109145072 | 0.0133945495 | 0.551 | 0.449 |
| B09 | 8.119340e-07 | -0.0005326007 | 0.0004986745 | 0.367 | 0.633 |
# Negatively associated
function_predictions %>%
filter(mean <0) %>%
arrange(-negative_support) %>%
tt()| trait | mean | p1 | p9 | positive_support | negative_support |
|---|---|---|---|---|---|
| D08 | -1.093511e-03 | -0.002214746 | -2.281340e-04 | 0.032 | 0.968 |
| B03 | -1.057672e-02 | -0.017631148 | -3.229192e-03 | 0.045 | 0.955 |
| D06 | -3.229620e-03 | -0.006962169 | -5.139396e-05 | 0.098 | 0.902 |
| B04 | -7.665435e-03 | -0.017979073 | 1.779859e-03 | 0.149 | 0.851 |
| B06 | -6.778849e-03 | -0.016584138 | 2.083246e-03 | 0.171 | 0.829 |
| D07 | -1.166506e-02 | -0.029621342 | 4.098672e-03 | 0.194 | 0.806 |
| D03 | -4.540739e-03 | -0.012618543 | 2.717105e-03 | 0.207 | 0.793 |
| D05 | -1.909669e-03 | -0.007386594 | 2.833109e-03 | 0.221 | 0.779 |
| B02 | -3.789939e-03 | -0.012493886 | 4.029648e-03 | 0.247 | 0.753 |
| S03 | -9.214706e-03 | -0.031916056 | 1.659255e-02 | 0.249 | 0.751 |
| D09 | -1.644702e-03 | -0.007831696 | 4.607589e-03 | 0.318 | 0.682 |
| S02 | -4.283466e-03 | -0.013942829 | 2.781315e-03 | 0.319 | 0.681 |
| B07 | -3.643201e-03 | -0.015378159 | 8.373293e-03 | 0.322 | 0.678 |
| D01 | -3.039883e-04 | -0.004674591 | 4.160412e-03 | 0.417 | 0.583 |
| B10 | -1.661264e-05 | -0.000312485 | 2.497855e-04 | 0.449 | 0.551 |
positive <- function_predictions %>%
filter(mean >0) %>%
arrange(mean) %>%
filter(positive_support>=0.9) %>%
dplyr::select(-negative_support) %>%
rename(support=positive_support)
negative <- function_predictions %>%
filter(mean <0) %>%
arrange(mean) %>%
filter(negative_support>=0.9) %>%
dplyr::select(-positive_support) %>%
rename(support=negative_support)
bind_rows(positive,negative) %>%
left_join(GIFT_db,by=join_by(trait==Code_function)) %>%
mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait)))) %>%
ggplot(aes(x=mean, y=fct_rev(trait), xmin=p1, xmax=p9, color=Function)) +
geom_point() +
geom_errorbar() +
xlim(c(-0.02,0.02)) +
geom_vline(xintercept=0) +
scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
theme_minimal() +
labs(x="Regression coefficient",y="Functional trait")8.3.2 Sex
8.3.2.1 Negatively associated genomes with male cats
# Compute variance partitioning
varpart=computeVariancePartitioning(m)
varpart$vals %>%
as.data.frame() %>%
rownames_to_column(var="variable") %>%
pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
mutate(variable=factor(variable, levels=rev(c("origin","sex","logseqdepth","Random: location")))) %>%
group_by(variable) %>%
summarise(mean=mean(value)*100,sd=sd(value)*100) %>%
tt()| variable | mean | sd |
|---|---|---|
| Random: location | 37.900015 | 25.317903 |
| logseqdepth | 56.110626 | 25.796874 |
| sex | 4.937460 | 5.612719 |
| origin | 1.051899 | 1.282563 |
# Select desired support threshold
support=0.9
negsupport=1-support
# Basal tree
postestimates_tree <- genome_tree
# Posterior estimate table
post_beta <- getPostEstimate(hM=m, parName="Beta")$support %>%
as.data.frame() %>%
mutate(variable=m$covNames) %>%
pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
mutate(genome=factor(genome, levels=rev(postestimates_tree$tip.label))) %>%
mutate(value = case_when(
value >= support ~ "Positive",
value <= negsupport ~ "Negative",
TRUE ~ "Neutral")) %>%
mutate(value=factor(value, levels=c("Positive","Neutral","Negative"))) %>%
pivot_wider(names_from = variable, values_from = value) %>%
column_to_rownames(var="genome")
getPostEstimate(hM=m, parName="Beta")$support %>%
as.data.frame() %>%
mutate(variable=m$covNames) %>%
pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
mutate(trend = case_when(
value >= support ~ "Positive",
value <= negsupport ~ "Negative",
TRUE ~ "Neutral")) %>%
filter(variable=="sexMale", trend=="Negative") %>%
arrange(-value) %>%
left_join(genome_metadata,by=join_by(genome==genome)) %>%
dplyr::select(genome,phylum,class,order,family,species,value) %>%
arrange(phylum, class, family)%>%
tt()| genome | phylum | class | order | family | species | value |
|---|---|---|---|---|---|---|
| bin_Denmark.4 | Actinomycetota | Coriobacteriia | Coriobacteriales | Coriobacteriaceae | Collinsella stercoris | 0.090 |
| bin_Aruba.14 | Actinomycetota | Coriobacteriia | Coriobacteriales | Coriobacteriaceae | Collinsella stercoris | 0.089 |
| bin_Brazil.91 | Bacillota | Bacilli | RF39 | UBA660 | CAG-988 sp003149915 | 0.082 |
| bin_Brazil.76 | Bacillota | Bacilli | RF39 | UBA660 | CAG-877 sp900554305 | 0.017 |
| bin_Malaysia.17 | Bacillota | Bacilli | RF39 | UBA660 | NA | 0.016 |
| bin_Brazil.45 | Bacillota | Bacilli | RF39 | UBA660 | CAG-877 sp900554315 | 0.010 |
| bin_Malaysia.16 | Bacillota_A | Clostridia | Oscillospirales | Acutalibacteraceae | NA | 0.085 |
| bin_Malaysia.78 | Bacillota_A | Clostridia | Oscillospirales | Acutalibacteraceae | Ruminococcus_E bromii_B | 0.081 |
| bin_Brazil.69 | Bacillota_A | Clostridia | Oscillospirales | Acutalibacteraceae | Clostridium_A leptum | 0.020 |
| bin_Brazil.83 | Bacillota_A | Clostridia | Lachnospirales | Anaerotignaceae | NA | 0.069 |
| bin_Denmark.63 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Anaerostipes hadrus | 0.091 |
| bin_Spain.11 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.084 |
| bin_Malaysia.3 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.077 |
| bin_Malaysia.45 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Blautia_A wexlerae | 0.077 |
| bin_Brazil.1 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.071 |
| bin_Malaysia.7 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | CAG-317 sp900543415 | 0.070 |
| bin_Denmark.118 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Dorea_A longicatena | 0.066 |
| bin_Brazil.3 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Faecalimonas sp900555395 | 0.065 |
| bin_Malaysia.52 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Fusicatenibacter saccharivorans | 0.065 |
| bin_Brazil.89 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Faecalimonas sp900550235 | 0.062 |
| bin_Denmark.34 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Fusicatenibacter saccharivorans | 0.062 |
| bin_Malaysia.73 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.062 |
| bin_Brazil.166 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.057 |
| bin_Brazil.165 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Blautia_A caecimuris | 0.055 |
| bin_Denmark.19 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Faecalimonas sp900556835 | 0.053 |
| bin_Spain.53 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.053 |
| bin_Brazil.54 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Dorea_B phocaeensis | 0.051 |
| bin_Brazil.157 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.046 |
| bin_Brazil.32 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Mediterraneibacter torques | 0.042 |
| bin_Brazil.8 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Blautia_A sp900547615 | 0.042 |
| bin_Spain.6 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Schaedlerella glycyrrhizinilytica | 0.039 |
| bin_Denmark.109 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Eisenbergiella sp900550285 | 0.034 |
| bin_Brazil.113 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Anaerobutyricum sp900754855 | 0.031 |
| bin_Brazil.17 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.030 |
| bin_Spain.37 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | UMGS1472 sp900552095 | 0.029 |
| bin_Denmark.52 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Ruminococcus_B gnavus | 0.023 |
| bin_Brazil.56 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Ruminococcus_A sp000432335 | 0.022 |
| bin_Spain.67 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Ruminococcus_B sp900544395 | 0.021 |
| bin_Brazil.28 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.019 |
| bin_Brazil.93 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.019 |
| bin_Malaysia.110 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Catenibacillus sp900557175 | 0.018 |
| bin_Aruba.43 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Roseburia sp900548205 | 0.017 |
| bin_Malaysia.108 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.017 |
| bin_Brazil.116 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.016 |
| bin_Malaysia.86 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.015 |
| bin_Brazil.99 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Lachnospira sp900552795 | 0.013 |
| bin_CaboVerde.61 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Roseburia sp900548205 | 0.013 |
| bin_Brazil.97 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | UBA9502 sp900538475 | 0.010 |
| bin_Malaysia.46 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Blautia sp003287895 | 0.010 |
| bin_Brazil.50 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Lachnospira sp003451515 | 0.009 |
| bin_Denmark.66 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Blautia sp900120295 | 0.009 |
| bin_Spain.60 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.009 |
| bin_Malaysia.125 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Blautia sp900539145 | 0.008 |
| bin_CaboVerde.34 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.005 |
| bin_Aruba.13 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | CAG-81 sp000435795 | 0.002 |
| bin_Brazil.63 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.002 |
| bin_Denmark.62 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Clostridium_Q sp000435655 | 0.002 |
| bin_Brazil.105 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | NA | 0.001 |
| bin_Denmark.20 | Bacillota_A | Clostridia | Lachnospirales | Lachnospiraceae | Enterocloster sp001517625 | 0.001 |
| bin_Malaysia.98 | Bacillota_A | Clostridia | Oscillospirales | Oscillospiraceae | NA | 0.066 |
| bin_Malaysia.9 | Bacillota_A | Clostridia | Oscillospirales | Oscillospiraceae | Dysosmobacter welbionis | 0.065 |
| bin_Aruba.28 | Bacillota_A | Clostridia | Oscillospirales | Oscillospiraceae | NA | 0.044 |
| bin_Brazil.177 | Bacillota_A | Clostridia | Oscillospirales | Oscillospiraceae | NA | 0.044 |
| bin_Malaysia.116 | Bacillota_A | Clostridia | Oscillospirales | Oscillospiraceae | Flavonifractor plautii | 0.043 |
| bin_Malaysia.34 | Bacillota_A | Clostridia | Oscillospirales | Oscillospiraceae | Lawsonibacter sp000177015 | 0.040 |
| bin_Brazil.137 | Bacillota_A | Clostridia | Oscillospirales | Oscillospiraceae | NA | 0.027 |
| bin_Aruba.52 | Bacillota_A | Clostridia | Tissierellales | Peptoniphilaceae | NA | 0.094 |
| bin_Aruba.31 | Bacillota_A | Clostridia | Oscillospirales | Ruminococcaceae | Negativibacillus sp000435195 | 0.080 |
| bin_Malaysia.102 | Bacillota_A | Clostridia | Oscillospirales | Ruminococcaceae | NA | 0.080 |
| bin_Malaysia.30 | Bacillota_A | Clostridia | Oscillospirales | Ruminococcaceae | Phocea massiliensis | 0.055 |
| bin_Denmark.72 | Bacillota_C | Negativicutes | Selenomonadales | Selenomonadaceae | Megamonas funiformis | 0.082 |
| bin_Denmark.85 | Bacillota_C | Negativicutes | Selenomonadales | Selenomonadaceae | NA | 0.077 |
| bin_Brazil.5 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotella sp900540415 | 0.080 |
| bin_Malaysia.18 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotellamassilia sp000437675 | 0.051 |
| bin_Brazil.48 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Bacteroides sp900766005 | 0.050 |
| bin_Malaysia.117 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotellamassilia sp900541335 | 0.047 |
| bin_CaboVerde.18 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotella copri | 0.038 |
| bin_Aruba.10 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotella sp900544825 | 0.037 |
| bin_Malaysia.64 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Paraprevotella clara | 0.033 |
| bin_Brazil.163 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Prevotella lascolaii | 0.029 |
| bin_Spain.4 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Phocaeicola sp000436795 | 0.027 |
| bin_Spain.48 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Bacteroides fragilis | 0.019 |
| bin_Brazil.96 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Bacteroides uniformis | 0.014 |
| bin_Malaysia.105 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Phocaeicola massiliensis | 0.014 |
| bin_Spain.21 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Phocaeicola coprophilus | 0.012 |
| bin_Malaysia.121 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Bacteroides stercoris | 0.011 |
| bin_Brazil.43 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Phocaeicola vulgatus | 0.010 |
| bin_Denmark.30 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Phocaeicola sp900546645 | 0.010 |
| bin_Brazil.103 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Phocaeicola plebeius_A | 0.009 |
| bin_Malaysia.77 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Phocaeicola sp900542985 | 0.006 |
| bin_Brazil.6 | Bacteroidota | Bacteroidia | Bacteroidales | Bacteroidaceae | Phocaeicola coprocola | 0.005 |
| bin_Brazil.110 | Bacteroidota | Bacteroidia | Bacteroidales | Barnesiellaceae | Barnesiella intestinihominis | 0.024 |
| bin_Malaysia.13 | Bacteroidota | Bacteroidia | Bacteroidales | Muribaculaceae | NA | 0.075 |
| bin_Spain.23 | Bacteroidota | Bacteroidia | Bacteroidales | Muribaculaceae | CAG-279 sp900541935 | 0.051 |
| bin_CaboVerde.37 | Bacteroidota | Bacteroidia | Bacteroidales | Porphyromonadaceae | NA | 0.058 |
| bin_Brazil.11 | Bacteroidota | Bacteroidia | Bacteroidales | Rikenellaceae | Alistipes putredinis | 0.045 |
| bin_Malaysia.131 | Bacteroidota | Bacteroidia | Bacteroidales | Rikenellaceae | Alistipes putredinis | 0.043 |
| bin_Brazil.38 | Bacteroidota | Bacteroidia | Bacteroidales | Rikenellaceae | Alistipes communis | 0.039 |
| bin_Brazil.86 | Bacteroidota | Bacteroidia | Bacteroidales | Rikenellaceae | Alistipes dispar | 0.030 |
| bin_Brazil.138 | Bacteroidota | Bacteroidia | Bacteroidales | Tannerellaceae | Parabacteroides sp000436495 | 0.023 |
| bin_Brazil.124 | Bacteroidota | Bacteroidia | Bacteroidales | Tannerellaceae | NA | 0.019 |
| bin_Brazil.160 | Bacteroidota | Bacteroidia | Bacteroidales | Tannerellaceae | Parabacteroides distasonis | 0.013 |
| bin_CaboVerde.10 | Campylobacterota | Campylobacteria | Campylobacterales | Campylobacteraceae | NA | 0.070 |
| bin_Brazil.68 | Campylobacterota | Campylobacteria | Campylobacterales | Campylobacteraceae | Campylobacter_D helveticus | 0.049 |
| bin_Spain.26 | Campylobacterota | Campylobacteria | Campylobacterales | Campylobacteraceae | Campylobacter_D upsaliensis | 0.047 |
| bin_Brazil.145 | Cyanobacteria | Vampirovibrionia | Gastranaerophilales | Gastranaerophilaceae | NA | 0.044 |
| bin_Brazil.140 | Fusobacteriota | Fusobacteriia | Fusobacteriales | Fusobacteriaceae | Fusobacterium_A sp900543175 | 0.029 |
| bin_Malaysia.6 | Fusobacteriota | Fusobacteriia | Fusobacteriales | Fusobacteriaceae | Fusobacterium_B sp900545035 | 0.015 |
| bin_Brazil.159 | Fusobacteriota | Fusobacteriia | Fusobacteriales | Fusobacteriaceae | Fusobacterium_B sp900541465 | 0.012 |
| bin_Brazil.146 | Pseudomonadota | Alphaproteobacteria | RF32 | CAG-239 | CAG-495 sp001917125 | 0.074 |
| bin_Malaysia.50 | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Succinivibrionaceae | NA | 0.084 |
| bin_Brazil.82 | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Succinivibrionaceae | Anaerobiospirillum succiniciproducens | 0.046 |
| bin_CaboVerde.33 | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Succinivibrionaceae | Anaerobiospirillum sp900543125 | 0.045 |
| bin_CaboVerde.55 | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Succinivibrionaceae | Anaerobiospirillum_A thomasii | 0.036 |
| bin_Brazil.111 | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Succinivibrionaceae | Succinivibrio sp000431835 | 0.024 |
8.3.2.2 Estimate - support plot
estimate <- getPostEstimate(hM=m, parName="Beta")$mean %>%
as.data.frame() %>%
mutate(variable=m$covNames) %>%
filter(variable=="sexMale") %>%
pivot_longer(!variable, names_to = "genome", values_to = "mean") %>%
dplyr::select(genome,mean)
support <- getPostEstimate(hM=m, parName="Beta")$support %>%
as.data.frame() %>%
mutate(variable=m$covNames) %>%
filter(variable=="sexMale") %>%
pivot_longer(!variable, names_to = "genome", values_to = "support") %>%
dplyr::select(genome,support)
phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
mutate(phylum=str_remove_all(phylum, "p__")) %>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
filter(genome %in% estimate$genome) %>%
arrange(match(genome, estimate$genome)) %>%
dplyr::select(phylum, colors) %>%
unique() %>%
arrange(phylum) %>%
dplyr::select(colors) %>%
pull()
inner_join(estimate,support,by=join_by(genome==genome)) %>%
mutate(significance=ifelse(support >= 0.9 | support <= 0.1,1,0)) %>%
mutate(support=ifelse(mean<0,1-support,support)) %>%
left_join(genome_metadata, by = join_by(genome == genome)) %>%
mutate(phylum = ifelse(support > 0.9, phylum, NA)) %>%
ggplot(aes(x=mean,y=support,color=phylum))+
geom_point(alpha=0.7, shape=16, size=3)+
scale_color_manual(values = phylum_colors) +
geom_vline(xintercept = 0) +
xlim(c(-0.4,0.4)) +
labs(x = "Beta regression coefficient", y = "Posterior probability") +
theme_minimal()+
theme(legend.position = "none")8.3.2.3 Predict responses
# Select modelchain of interest
load("hmsc/fit_model1_250_10.Rdata")
gradient = c("Male","Female","Unknown")
gradientlength = length(gradient)
#Treatment-specific gradient predictions
pred <- constructGradient(m,
focalVariable = "sex",
non.focalVariables = list(logseqdepth=list(1),location=list(1))) %>%
predict(m, Gradient = ., expected = TRUE) %>%
do.call(rbind,.) %>%
as.data.frame() %>%
mutate(sex=rep(gradient,1000)) %>%
pivot_longer(!sex,names_to = "genome", values_to = "value")# weights: 4 (3 variable)
initial value 63.769541
final value 61.728471
converged
8.3.2.4 Element level
elements_table <- genome_gifts %>%
to.elements(., GIFT_db) %>%
as.data.frame()
community_elements <- pred %>%
group_by(sex, genome) %>%
mutate(row_id = row_number()) %>%
pivot_wider(names_from = genome, values_from = value) %>%
ungroup() %>%
group_split(row_id) %>%
as.list() %>%
lapply(., FUN = function(x){x %>%
dplyr::select(-row_id) %>%
column_to_rownames(var = "sex") %>%
as.data.frame() %>%
exp() %>%
t() %>%
tss() %>%
to.community(elements_table,.,GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="sex")
})
calculate_slope <- function(x) {
lm_fit <- lm(unlist(x) ~ seq_along(unlist(x)))
coef(lm_fit)[2]
}
element_predictions <- map_dfc(community_elements, function(mat) {
mat %>%
column_to_rownames(var = "sex") %>%
t() %>%
as.data.frame() %>%
rowwise() %>%
mutate(slope = calculate_slope(c_across(everything()))) %>%
dplyr::select(slope) }) %>%
t() %>%
as.data.frame() %>%
set_names(colnames(community_elements[[1]])[-1]) %>%
rownames_to_column(var="iteration") %>%
pivot_longer(!iteration, names_to="trait",values_to="value") %>%
group_by(trait) %>%
summarise(mean=mean(value),
p1 = quantile(value, probs = 0.1),
p9 = quantile(value, probs = 0.9),
positive_support = sum(value > 0)/1000,
negative_support = sum(value < 0)/1000) %>%
arrange(-positive_support)8.3.2.4.1 Positive associated functions at element level
# Positively associated
unique_funct_db<- GIFT_db[c(2,4,5,6)] %>%
distinct(Code_element, .keep_all = TRUE)
element_predictions %>%
filter(mean >0) %>%
arrange(-positive_support) %>%
filter(positive_support>=0.9) %>%
left_join(unique_funct_db, by = join_by(trait == Code_element))%>%
arrange(Domain,Function)# A tibble: 30 × 9
trait mean p1 p9 positive_support negative_support Domain Function Element
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
1 B0220 0.00893 0.00137 0.0165 0.924 0.076 Biosynthesis Amino acid biosynthesis Beta-alanine
2 B0212 0.0293 0.00379 0.0522 0.922 0.078 Biosynthesis Amino acid biosynthesis Arginine
3 B0218 0.0110 0.000319 0.0224 0.902 0.098 Biosynthesis Amino acid biosynthesis Tyrosine
4 B0310 0.0183 0.00536 0.0312 0.964 0.036 Biosynthesis Amino acid derivative biosynthesis Tryptamine
5 B0303 0.0145 0.00204 0.0255 0.924 0.076 Biosynthesis Amino acid derivative biosynthesis Ectoine
6 B0307 0.0418 0.00475 0.0711 0.914 0.086 Biosynthesis Amino acid derivative biosynthesis Spermidine
7 B0901 0.00118 0.000115 0.00255 0.917 0.083 Biosynthesis Metallophore biosynthesis Staphyloferrin
8 B0105 0.0262 0.0106 0.0428 0.948 0.052 Biosynthesis Nucleic acid biosynthesis ADP/ATP
9 B0104 0.0351 0.0102 0.0581 0.942 0.058 Biosynthesis Nucleic acid biosynthesis CDP/CTP
10 B0106 0.0150 0.00305 0.0267 0.921 0.079 Biosynthesis Nucleic acid biosynthesis GDP/GTP
# ℹ 20 more rows
8.3.2.4.2 Negatively associated functions at element level
element_predictions %>%
filter(mean <0) %>%
arrange(-negative_support) %>%
filter(negative_support>=0.9) %>%
left_join(unique_funct_db, by = join_by(trait == Code_element))%>%
arrange(Domain,Function)# A tibble: 22 × 9
trait mean p1 p9 positive_support negative_support Domain Function Element
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
1 B0216 -0.0486 -0.0729 -0.0249 0.026 0.974 Biosynthesis Amino acid biosynthesis Tryptophan
2 B0215 -0.0591 -0.0903 -0.0279 0.03 0.97 Biosynthesis Amino acid biosynthesis Histidine
3 B0208 -0.0292 -0.0541 -0.00505 0.069 0.931 Biosynthesis Amino acid biosynthesis Valine
4 B0209 -0.0283 -0.0555 -0.00322 0.073 0.927 Biosynthesis Amino acid biosynthesis Isoleucine
5 B1012 -0.00833 -0.0130 -0.00368 0.005 0.995 Biosynthesis Antibiotic biosynthesis Fosfomycin
6 B1004 -0.00460 -0.00875 -0.000640 0.081 0.919 Biosynthesis Antibiotic biosynthesis Bacilysin
7 B0604 -0.0259 -0.0515 -0.000348 0.098 0.902 Biosynthesis Organic anion biosynthesis L-lactate
8 B0704 -0.0583 -0.0890 -0.0273 0.013 0.987 Biosynthesis Vitamin biosynthesis Pantothenate (B5)
9 B0711 -0.0330 -0.0522 -0.0149 0.041 0.959 Biosynthesis Vitamin biosynthesis Menaquinone (K2)
10 B0710 -0.0179 -0.0297 -0.00596 0.057 0.943 Biosynthesis Vitamin biosynthesis Phylloquinone (K1)
# ℹ 12 more rows
positive <- element_predictions %>%
filter(mean >0) %>%
arrange(mean) %>%
filter(positive_support>=0.9) %>%
dplyr::select(-negative_support) %>%
rename(support=positive_support)
negative <- element_predictions %>%
filter(mean <0) %>%
arrange(mean) %>%
filter(negative_support>=0.9) %>%
dplyr::select(-positive_support) %>%
rename(support=negative_support)
bind_rows(positive,negative) %>%
left_join(GIFT_db,by=join_by(trait==Code_element)) %>%
mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait)))) %>%
ggplot(aes(x=mean, y=fct_rev(trait), xmin=p1, xmax=p9, color=Function)) +
geom_point() +
geom_errorbar() +
# xlim(c(-0.04,0.04)) +
geom_vline(xintercept=0) +
# scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
theme_minimal() +
labs(x="Regression coefficient",y="Functional trait")8.3.2.5 Function level
functions_table <- elements_table %>%
to.functions(., GIFT_db) %>%
as.data.frame()
community_functions <- pred %>%
group_by(sex, genome) %>%
mutate(row_id = row_number()) %>%
pivot_wider(names_from = genome, values_from = value) %>%
ungroup() %>%
group_split(row_id) %>%
as.list() %>%
lapply(., FUN = function(x){x %>%
dplyr::select(-row_id) %>%
column_to_rownames(var = "sex") %>%
as.data.frame() %>%
exp() %>%
t() %>%
tss() %>%
to.community(functions_table,.,GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="sex")
})#max-min option
calculate_slope <- function(x) {
lm_fit <- lm(unlist(x) ~ seq_along(unlist(x)))
coef(lm_fit)[2]
}
function_predictions <- map_dfc(community_functions, function(mat) {
mat %>%
column_to_rownames(var = "sex") %>%
t() %>%
as.data.frame() %>%
rowwise() %>%
mutate(slope = calculate_slope(c_across(everything()))) %>%
dplyr::select(slope) }) %>%
t() %>%
as.data.frame() %>%
set_names(colnames(community_functions[[1]])[-1]) %>%
rownames_to_column(var="iteration") %>%
pivot_longer(!iteration, names_to="trait",values_to="value") %>%
group_by(trait) %>%
summarise(mean=mean(value),
p1 = quantile(value, probs = 0.1),
p9 = quantile(value, probs = 0.9),
positive_support = sum(value > 0)/1000,
negative_support = sum(value < 0)/1000) %>%
arrange(-positive_support)
# Positively associated
function_predictions %>%
filter(mean >0) %>%
arrange(-positive_support) %>%
tt()| trait | mean | p1 | p9 | positive_support | negative_support |
|---|---|---|---|---|---|
| D07 | 0.0375944479 | 1.972014e-02 | 0.056148600 | 0.992 | 0.008 |
| B01 | 0.0120235113 | 2.924545e-03 | 0.021231683 | 0.939 | 0.061 |
| B03 | 0.0134706279 | 3.129619e-03 | 0.021856061 | 0.932 | 0.068 |
| D06 | 0.0046369186 | 1.904574e-04 | 0.009455340 | 0.909 | 0.091 |
| B09 | 0.0008808116 | 2.773819e-05 | 0.001863172 | 0.905 | 0.095 |
| D03 | 0.0086358818 | -2.117773e-03 | 0.018844548 | 0.874 | 0.126 |
| B04 | 0.0086819802 | -1.265781e-03 | 0.019197647 | 0.866 | 0.134 |
| D05 | 0.0037777625 | -6.379054e-03 | 0.011495393 | 0.822 | 0.178 |
| B06 | 0.0095293505 | -4.760244e-03 | 0.023419874 | 0.812 | 0.188 |
| D09 | 0.0044482653 | -4.066690e-03 | 0.012324899 | 0.808 | 0.192 |
| S03 | 0.0154206703 | -1.668538e-02 | 0.041133011 | 0.800 | 0.200 |
| D08 | 0.0003742537 | -4.028928e-04 | 0.001116957 | 0.691 | 0.309 |
| D01 | 0.0007972038 | -4.891676e-03 | 0.006278674 | 0.668 | 0.332 |
# Negatively associated
function_predictions %>%
filter(mean <0) %>%
arrange(-negative_support) %>%
tt()| trait | mean | p1 | p9 | positive_support | negative_support |
|---|---|---|---|---|---|
| B10 | -0.0006304998 | -0.0009540545 | -0.0003081384 | 0.020 | 0.980 |
| D02 | -0.0151032927 | -0.0273709242 | -0.0026929023 | 0.061 | 0.939 |
| S01 | -0.0134498482 | -0.0281931644 | 0.0021095325 | 0.132 | 0.868 |
| B07 | -0.0093150264 | -0.0210303996 | 0.0025766178 | 0.142 | 0.858 |
| S02 | -0.0039566793 | -0.0121727363 | 0.0061940272 | 0.170 | 0.830 |
| B02 | -0.0035889597 | -0.0161455535 | 0.0073469073 | 0.362 | 0.638 |
| B08 | -0.0017867040 | -0.0149536701 | 0.0101457248 | 0.533 | 0.467 |
positive <- function_predictions %>%
filter(mean >0) %>%
arrange(mean) %>%
filter(positive_support>=0.9) %>%
dplyr::select(-negative_support) %>%
rename(support=positive_support)
negative <- function_predictions %>%
filter(mean <0) %>%
arrange(mean) %>%
filter(negative_support>=0.9) %>%
dplyr::select(-positive_support) %>%
rename(support=negative_support)
bind_rows(positive,negative) %>%
left_join(GIFT_db,by=join_by(trait==Code_function)) %>%
mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait)))) %>%
ggplot(aes(x=mean, y=fct_rev(trait), xmin=p1, xmax=p9, color=Function)) +
geom_point() +
geom_errorbar() +
# xlim(c(-0.02,0.02)) +
geom_vline(xintercept=0) +
scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
theme_minimal() +
labs(x="Regression coefficient",y="Functional trait")